SoroushVahidi/scidocs huggingface dataset:
A Customizable Framework for Prioritizing Systems Security Engineering Processes, Activities, and Tasks
(2017)

As modern systems become increasingly complex, current security practices lack effective methodologies to adequately address the system security. This paper proposes a repeatable and tailorable framework to assist in the application of systems security engineering (SSE) processes, activities, and tasks as defined in the recently released National Institute of Standards and Technology (NIST) Special Publication 800–160. First, a brief survey of systems-oriented security methodologies is provided. Next, an examination of the relationships between the NIST-defined SSE processes is conducted to provide context for the engineering problem space. These findings inform a mapping of the NIST SSE processes to seven system-agnostic security domains which enable prioritization for three types of systems (conventional IT, cyber-physical, and defense). These concrete examples provide further understanding for applying and prioritizing the SSE effort. The goal of this paper is assist practitioners by informing the efficient application of the 30 processes, 111 activities, and 428 tasks defined in NIST SP 800–160. The customizable framework tool is available online for developers to employ, modify, and tailor to meet their needs.
Internet of Things : Architectural Framework for eHealth Security
(2014)

The Internet of Things (IoT) holds big promises for Health Care, especially in Proactive Personal eHealth. To fulfil the promises major challenges must be overcome, particularly regarding privacy and security. This paper explores these issues, discusses use case scenarios, and advances a secure architecture framework. We close the paper with a discussion of the state of various standard organizations, in the conviction of the critical role they will play in making eHealth bloom.
Performance assessment of Multilateration Systems - a solution to nextgen surveillance
(2010)

Multilateration (MLAT) surveillance is now being used in all types of airspace for air traffic management. MLAT can be used for airport surface movement surveillance as well as for terminal and en route surveillance, using Wide Area Multilateration (WAM). MLAT is a low-cost technology that not only has major advantages in operations and maintenance, but also provides excellent performance under all conditions, especially for countries with large geographic areas or mountainous terrain to cover. This paper focuses on performance metrics from operational system data to demonstrate that the performance of Era's MLAT systems meet surveillance requirements for all surveillance applications. This includes the key performance requirements for the various surveillance applications that are evaluated, including accuracy, coverage, update rate, integrity and availability.
Beyond flat surface computing: challenges of depth-aware and curved interfaces
(2009)

In the past decade, multi-touch-sensitive interactive surfaces have transitioned from pure research prototypes in the lab, to commercial products with wide-spread adoption. One of the longer term visions of this research follows the idea of ubiquitous computing, where everyday surfaces in our environment are made interactive. However, most of current interfaces remain firmly tied to the traditional flat rectangular displays of the today's computers and while they benefit from the directness and the ease of use, they are often not much more than touch-enabled standard desktop interfaces.
 In this paper, we argue for explorations that transcend the traditional notion of the flat display, and envision interfaces that are curved, three-dimensional, or that cross the boundary between the digital and physical world. In particular, we present two research directions that explore this idea: (a) exploring the three-dimensional interaction space above the display and (b) enabling gestural and touch interactions on curved devices for novel interaction possibilities. To illustrate both of these, we draw examples from our own work and the work of others, and guide the reader through several case studies that highlight the challenges and benefits of such novel interfaces. The implications on media requirements and collaboration aspects are discussed in detail, and, whenever possible, we highlight promising directions of future research. We believe that the compelling application design for future non-flat user interfaces will greatly depend on exploiting the unique characteristics of the given form factor.
Body Area Sensor Networks: Requirements, Operations, and Challenges
(2014)

This article provides an overview of body area sensor networks (BASNs), an application of wireless technology that changed health care to suit the comfort of the population. There are various applications of BASNs and attempts have been made to make the human body a channel for wireless communication. BASNs employ a three-tiered architectural system that requires various technical requirements for its optimal and efficient operation. Energy consumption is one of the major issues that is currently being addressed through self-harvesting and many other techniques. Even with the benefits at hand, there are various issues such as interference and eavesdropping that BASNs have to tackle. Biometrics is a widely used solution. Researchers are also working on various ambitious projects that deal with improving deep brain simulation, heart regulation, drug delivery, and prosthetic actuation to use BASN effectively.
DeepMind Lab
(2016)

DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.
Deep Learning for Practical Image Recognition: Case Study on Kaggle Competitions
(2018)

In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical image recognition applications as compared to using standard deep learning methods. The proposed framework has recently been deployed as the key kernel for several image recognition competitions organized by Kaggle. The performance is promising as our final private scores were ranked 4 out of 2293 teams for fish recognition on the challenge "The Nature Conservancy Fisheries Monitoring" and 3 out of 834 teams for cervix recognition on the challenge "Intel &MobileODT Cervical Cancer Screening", and several others. We believe that by sharing the solutions, we can further promote the applications of deep learning techniques.
Overview of the EVS codec architecture
(2015)

The recently standardized 3GPP codec for Enhanced Voice Services (EVS) offers new features and improvements for low-delay real-time communication systems. Based on a novel, switched low-delay speech/audio codec, the EVS codec contains various tools for better compression efficiency and higher quality for clean/noisy speech, mixed content and music, including support for wideband, super-wideband and full-band content. The EVS codec operates in a broad range of bitrates, is highly robust against packet loss and provides an AMR-WB interoperable mode for compatibility with existing systems. This paper gives an overview of the underlying architecture as well as the novel technologies in the EVS codec and presents listening test results showing the performance of the new codec in terms of compression and speech/audio quality.
Interaction techniques using head gaze for virtual reality
(2016)

Virtual Reality (VR) has finally caught the attention of major technology companies, giving it a significant boost towards becoming market-ready. VR has the capacity to create profoundly engaging experiences by synthesizing highly detailed sensory input in real time, making games the most obvious applications of VR. While the promises of VR may be exciting due to the problems it can solve and new experiences it can offer, there are still numerous issues to be addressed in order to achieve maximal immersion in VR. In this paper, we present the issues encountered during open testing of our applications on commercially available VR goggles and hand input device. The inability to effectively use the hands and feet to control and navigate the environment degrades the level of immersion of VR. With users preferring reliability over intuitiveness, we explore the use of gaze as a substitute input device. Results of experiments conducted on numerous volunteers showed significant improvement in terms of user control.
Classification for Fraud Detection with Social Network Analysis
(2009)

Worldwide fraud conducts to big losses to states’ treasuries and to private companies. Because of that, motivations to detect and fight fraud are high, but despite continuous efforts, it is far from being accomplished. The problems faced when trying to characterize fraud activities are many, with the specificities of fraud on each business domain leading the list. Despite the differences, building a classifier for fraud detection almost always requires to deal with unbalanced datasets, since fraudulent records are usually in a small number when compared with the nonfraudulent ones. This work describes two types of techniques to deal with fraud detection: techniques at a preprocessing level where the goal is to balance the dataset, and techniques at a processing level where the objective is to apply different errors costs to fraudulent and non-fraudulent cases. Besides that, as organizations and people more often do associations in order to commit fraud, is proposed a new method to make use of that information to improve the training of classifiers for fraud detection. In particular, this new method identifies patterns among the social networks for fraudulent organizations, and uses them to enrich the description of its entity. The enriched data will then be used jointly with balancing techniques to produce a better classifier to identify fraud.
Sentiment analysis of students' comment using lexicon based approach
(2017)

In education system, students' feedback is important to measure the quality of teaching. Students' feedback can be analyzed using lexicon based approach to identify the students' positive or negative attitude. In most of the existing teaching evaluation system, the intensifier words and blind negation words are not considered. The level of opinion result isn't displayed: whether positive or negative opinion. To address this problem, we propose to analyze the students' text feedback automatically using lexicon based approach to predict the level of teaching performance. A database of English sentiment words is created as a lexical source to get the polarity of words. By analyzing the sentiment information including intensifier words extracting from students' feedback, we are able to determine opinion result of teachers, describing the level of positive or negative opinions. This system shows the opinion result of teachers that is represented as to whether strongly positive, moderately positive, weakly positive, strongly negative, moderately negative, weakly negative or neutral.
The task-based approach : some questions and suggestions
(2004)

This article >rst addresses the question of what tasks are. It suggests that rather than accept the common ‘communicative’ de>nition, we should return to a broader de>nition and then focus on key dimensions that distinguish (from the learner’s perspective) di=erent types of task, notably degrees of taskinvolvement and degrees of focus on form or meaning. This approach helps us to conceptualize the complementary roles of form-focused and meaningfocused tasks in our methodology. It also shows the continuity between taskbased language teaching and the broader communicative approach within which it is a development. Finally the article asks whether ‘task-based approach’ is really the most appropriate term at all for describing these developments in language pedagogy.
Go with the Flow: When Listeners Use Music as Technology
(2016)

Music has been shown to have a profound effect on listeners’ internal states as evidenced by neuroscience research. Listeners report selecting and listening to music with specific intent, thereby using music as a tool to achieve desired psychological effects within a given context. In light of these observations, we argue that music information retrieval research must revisit the dominant assumption that listening to music is only an end unto itself. Instead, researchers should embrace the idea that music is also a technology used by listeners to achieve a specific desired internal state, given a particular set of circumstances and a desired goal. This paper focuses on listening to music in isolation (i.e., when the user listens to music by themselves with headphones) and surveys research from the fields of social psychology and neuroscience to build a case for a new line of research in music information retrieval on the ability of music to produce flow states in listeners. We argue that interdisciplinary collaboration is necessary in order to develop the understanding and techniques necessary to allow listeners to exploit the full potential of music as psychological technology.
A single neuron PID control for twin rotor MIMO system
(2009)

This paper presents an intelligent control scheme which utilizes a single neuron PID controller to an experimental propeller setup called the twin rotor multi-input multi-output system (TRMS). The control objective is to make the TRMS move quickly and accurately to the desired attitudes. The pitch angle and the azimuth angle in the conditions of decoupled and cross-coupled between vertical and horizontal axes are considered. It is difficult to design a suitable controller because of the influence between two axes and nonlinear movement. For easy demonstrate the movement on the vertical plane and horizontal plane separately, the TRMS is decoupled first by the main rotor and tail rotor. After successful simulations in decoupled condition, the more difficult simulations of cross-coupled condition are performed. Simulation results show that the new approach can improve the tracking performance.
Affinity Studies between Drugs and Clays as Adsorbent Material
(2017)

Pharmaceuticals, with veterinary and human usage, have continuously been launched into the environment and their presence has been frequently detected in water bodies. The inefficacy of conventional water treatment processes for the removal of drugs, added to their potential adverse effects to human health and environment, suggest that new separation processes should be studied. Adsorption is highlighted as a promising method and the use of alternative adsorbents is encouraged due to the high costs of activated carbon. Different clay materials were evaluated in the present work for the removal of amoxicillin, caffeine, propranolol, and diclofenac sodium from aqueous solutions. The removal efficiency depended on the drug and adsorbent material used and varied between 23-98 % for amoxicillin, 21-89 % for caffeine, 29-100 % for propranolol, and 2-99 % for diclofenac sodium. The results showed that clays may be used successfully as alternative adsorbent material on the removal of selected emergent contaminants.
Some Fundamental Limits on Cognitive Radio
(2004)

Cognitive radio refers to wireless architectures in which a communication system does not operate in a fixed assigned band, but rather searches and finds an appropriate band in which to operate. In this paper we explore, from first principles, the fundamental requirements for such system that tries to avoid interference to potential primary users of a band. We first show that in order to deliver real gains, cognitive radios must be able to detect undecodable signals. This is done by showing how to evaluate the tradeoff between secondary user power, available space for secondary operation, and interference protection for the primary receivers. We prove that in general, the performance of the optimal detector for detecting a weak unknown signal from a known zero-mean constellation is like that of the energy detector (radiometer). However we show that the presence of a known pilot signal can help greatly. We further motivate the need for pilot signals by showing that the radiometer is rendered useless by just moderate noise uncertainty. Finally, we show that quantization combined with noise uncertainty can make the detection of signals by any detector absolutely impossible below a certain SNR threshold. 1 Description of problem and some fundamental geographic tradeoffs The enormous success of the ISM bands has strengthened criticism of the FCC’s traditional process which allocates bands to a single use, issues exclusive licenses to a single entity within a geographical area, and prohibits other devices from transmitting significant power within these bands. As a result, the FCC is considering revising its spectrum allocation policies [1], and is moving ahead with the process. Cognitive radios are one proposed idea to take advantage of a more open spectrum policy.[2, 3]. A cognitive radio would be designed to dynamically adapt its transmissions to find and utilize frequencies while minimizing interference. This is inspired by actual measurements showing that most of the allocated spectrum is vastly underutilized [4]. Cognitive radios promise great societal benefits by allowing new applications that can flexibly use what spectrum happens to be available. However, fundamental theoretical questions remain as to the exact requirements for engineering a practical cognitive radio system. For illustrative purposes, consider the world depicted in (1a). Each of the two antennas represents a bustling metropolis. Under current regulations, users in the middle of nowhere are prohibited from using these spectrum bands, even if there are no receivers in the vicinity that might be interfered with. However, cognitive radios may someday be allowed to share the spectrum, on the condition that they not interfere with primary users. These primary users may be providing more socially important services, or they might simply be legacy systems that are unable to change. Figure (1b) depicts the primary transmitter in one metropolis. The dotted circle represents the boundary of decodability for a single-user system. That is, in the absence of all interference, 1
To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos
(2016)

Thumbnails play such an important role in online videos. As the most representative snapshot, they capture the essence of a video and provide the first impression to the viewers; ultimately, a great thumbnail makes a video more attractive to click and watch. We present an automatic thumbnail selection system that exploits two important characteristics commonly associated with meaningful and attractive thumbnails: high relevance to video content and superior visual aesthetic quality. Our system selects attractive thumbnails by analyzing various visual quality and aesthetic metrics of video frames, and performs a clustering analysis to determine the relevance to video content, thus making the resulting thumbnails more representative of the video. On the task of predicting thumbnails chosen by professional video editors, we demonstrate the effectiveness of our system against six baseline methods, using a real-world dataset of 1,118 videos collected from Yahoo Screen. In addition, we study what makes a frame a good thumbnail by analyzing the statistical relationship between thumbnail frames and non-thumbnail frames in terms of various image quality features. Our study suggests that the selection of a good thumbnail is highly correlated with objective visual quality metrics, such as the frame texture and sharpness, implying the possibility of building an automatic thumbnail selection system based on visual aesthetics.
Improving homograph disambiguation with supervised machine learning
(2018)

We describe a pre-existing rule-based homograph disambiguation system used for text-to-speech synthesis at Google, and compare it to a novel system which performs disambiguation using classifiers trained on a small amount of labeled data. An evaluation of these systems, using a new, freely available English data set, finds that hybrid systems (making use of both rules and machine learning) are significantly more accurate than either hand-written rules or machine learning alone. The evaluation also finds minimal performance degradation when the hybrid system is configured to run on limited-resource mobile devices rather than on production servers. The two best systems described here are used for homograph disambiguation on all US English text-to-speech traffic at Google.
Online LCL filter compensation using embedded FRA
(2014)

LCL filter control design is challenging because of presence of a resonant pole in the LCL filter transfer function. This paper presents a guideline for LCL filter design, which are often used in grid connected applications, with compensation based on complex poles and zeros. A Software Frequency Response Analyzer (SFRA) is integrated in the control software of the power converter. This enables periodical collection of frequency response data, which can be used to monitor movement of resonant poles due to aging, drifts and degradation of components. The information obtained from embedded SFRA can then be used to adjust the compensation. A feedback linearization method is illustrated to account for the grid voltage disturbances, which cannot be compensated by traditional PI controller. A solar micro inverter board is designed to prove the proposed scheme. Discussions about the embedded design challenges in integration of multiple power stage control, for a micro inverter using a single processor, are included and solutions presented.